{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析Z500上4DVar的调参结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from src.utils.plot import subplot_daloop\n",
    "from src.utils.data_utils import NAME_TO_VAR\n",
    "from scipy.signal import exponential\n",
    "import torch\n",
    "from src.models.forecast.forecast_module import ForecastLitModule\n",
    "from src.models.assimilate.fdvarcyclegan_assim_module import AssimilateLitModule as FdvarCycleGANAssimModule\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DALOOP_DIR = \"../../output/da_loop\"\n",
    "ERA5_DIR = \"../../data/era5\"\n",
    "BACKGRDOUND_DIR = \"../../data/background\"\n",
    "OBS_DIR = \"../../data/obs\"\n",
    "MASK_DIR = \"../../data/obs_mask\"\n",
    "VARIABLE = \"geopotential\"\n",
    "LEVEL = 500\n",
    "RESOLUTION = 5.625\n",
    "CKPT_DIR = \"../../ckpt/lt72\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = xr.open_mfdataset(f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/test/*.nc\", combine=\"by_coords\")\n",
    "xb = xr.open_mfdataset(f\"{BACKGRDOUND_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/test/*.nc\", combine=\"by_coords\")\n",
    "obs = xr.open_mfdataset(f\"{OBS_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/test/*.nc\", combine=\"by_coords\")\n",
    "obs_mask = xr.open_mfdataset(f\"{MASK_DIR}/test/*.nc\", combine=\"by_coords\")\n",
    "mean = np.load(os.path.join(f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_5.625deg\", 'normalize_mean.npy'))\n",
    "std = np.load(os.path.join(f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_5.625deg\", 'normalize_std.npy'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制同化预报循环误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:268: UserWarning: Attribute 'net' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['net'])`.\n",
      "  rank_zero_warn(\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:268: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
      "  rank_zero_warn(\n"
     ]
    }
   ],
   "source": [
    "forecast_model = ForecastLitModule.load_from_checkpoint(f\"{CKPT_DIR}/afnonet_z500.ckpt\",\n",
    "                                                        mean_path=f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/normalize_mean.npy\",\n",
    "                                                        std_path=f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/normalize_std.npy\",\n",
    "                                                        clim_paths=[\n",
    "                                                            f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/train/climatology.npy\",\n",
    "                                                            f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/val/climatology.npy\",\n",
    "                                                            f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/test/climatology.npy\",\n",
    "                                                        ]\n",
    ")\n",
    "forecast_net = forecast_model.net.cuda().eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mult = forecast_model.mult\n",
    "clim = forecast_model.clims[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:268: UserWarning: Attribute 'g_A2B' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['g_A2B'])`.\n",
      "  rank_zero_warn(\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:268: UserWarning: Attribute 'g_B2A' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['g_B2A'])`.\n",
      "  rank_zero_warn(\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:268: UserWarning: Attribute 'd_A' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['d_A'])`.\n",
      "  rank_zero_warn(\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:268: UserWarning: Attribute 'd_B' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['d_B'])`.\n",
      "  rank_zero_warn(\n"
     ]
    }
   ],
   "source": [
    "assim_model = FdvarCycleGANAssimModule.load_from_checkpoint(f\"{CKPT_DIR}/4dvarcyclegan_wscale_assim_z500.ckpt\",\n",
    "                                                            mean_path=f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/normalize_mean.npy\",\n",
    "                                                            std_path=f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/normalize_std.npy\",\n",
    "                                                            clim_paths=[\n",
    "                                                                f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/train/climatology.npy\",\n",
    "                                                                f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/val/climatology.npy\",\n",
    "                                                                f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/test/climatology.npy\",\n",
    "                                                            ],\n",
    "                                                            pred_ckpt=f\"{CKPT_DIR}/afnonet_z500.ckpt\",\n",
    "                                                            )\n",
    "assim_net = assim_model.g_A2B.cuda().eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xb = xb.sel(init_time=-72)[\"z\"].values[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_data = torch.from_numpy((xb - mean) / std).to(\"cuda:0\", dtype=torch.float32)\n",
    "input_obs = torch.unsqueeze(torch.from_numpy((obs[\"z\"].values[0:3]- mean) / std), dim=0).to(\"cuda:0\", dtype=torch.float32)\n",
    "input_mask = torch.unsqueeze(torch.from_numpy(obs_mask[\"mask\"].values[0:3]), dim=0).to(\"cuda:0\", dtype=torch.float32)\n",
    "input_obs = input_obs * input_mask\n",
    "with torch.set_grad_enabled(True):\n",
    "    x_k = torch.autograd.Variable(init_data, requires_grad=True)\n",
    "    raw_inc, scale_inc = assim_net.solve_increment(x_k, input_obs, input_mask)\n",
    "raw_inc = raw_inc.detach().cpu().numpy() * std\n",
    "scale_inc = scale_inc.detach().cpu().numpy() * std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_raw_inc = xr.DataArray(raw_inc[0,0], \n",
    "                          dims=[\"lat\", \"lon\"],\n",
    "                          coords={\"lat\": gt.lat, \n",
    "                                  \"lon\": gt.lon},\n",
    ")\n",
    "\n",
    "nc_scale_inc = xr.DataArray(scale_inc[0,0], \n",
    "                          dims=[\"lat\", \"lon\"],\n",
    "                          coords={\"lat\": gt.lat, \n",
    "                                  \"lon\": gt.lon},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:245: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(multi_line_string) > 1:\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:256: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:256: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:297: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  for line in multi_line_string:\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:364: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(p_mline) > 0:\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:402: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:402: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "I = nc_raw_inc.plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False, add_labels=False, rasterized=True, cmap='BrBG', vmin=np.min(raw_inc)/5, vmax=np.max(raw_inc)/5)\n",
    "cb = fig.colorbar(I, ax=ax, orientation='horizontal', pad=0.01, shrink=0.90)\n",
    "ax.coastlines(alpha=0.5)\n",
    "plt.savefig(f\"raw_inc_{NAME_TO_VAR[f'{VARIABLE}_{LEVEL}']}{LEVEL}.png\",dpi=300, bbox_inches=\"tight\")\n",
    "plt.savefig(f\"raw_inc_{NAME_TO_VAR[f'{VARIABLE}_{LEVEL}']}{LEVEL}.pdf\",dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:245: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(multi_line_string) > 1:\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:256: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:256: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:297: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  for line in multi_line_string:\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:364: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(p_mline) > 0:\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:402: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n",
      "/home/wangwx/anaconda3/envs/ddwp/lib/python3.8/site-packages/cartopy/crs.py:402: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "I = nc_scale_inc.plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False, add_labels=False, rasterized=True, cmap='BrBG', vmin=np.min(raw_inc)/5, vmax=np.max(raw_inc)/5)\n",
    "cb = fig.colorbar(I, ax=ax, orientation='horizontal', pad=0.01, shrink=0.90)\n",
    "ax.coastlines(alpha=0.5)\n",
    "plt.savefig(f\"scale_inc_{NAME_TO_VAR[f'{VARIABLE}_{LEVEL}']}{LEVEL}.png\",dpi=300, bbox_inches=\"tight\")\n",
    "plt.savefig(f\"scale_inc_{NAME_TO_VAR[f'{VARIABLE}_{LEVEL}']}{LEVEL}.pdf\",dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ddwp",
   "language": "python",
   "name": "ddwp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
